**Conflict of interest**

algorithm using global similarity distance, while content-based recommender has the worst performance. Popularity-based recommender algorithm is superior to cosine-based CF and content-based recommender in precision, but its coverages are too low (0.035 and 0.069), since popularity-based algorithm always recommends

Content-based 5 90 0.08 0.25 0.55 1.933 Content-based 10 100 0.12 0.20 1.01 1.76 Popularity-based 5 — 0.335 0.528 0.035 2.90 Popularity-based 10 — 0.509 0.400 0.069 2.729

The sparseness in crowdfunding platform Kickstarter is more than 99% [35]. With such a high sparseness, cosine-based CF obtains poor recommendation performance. Therefore, we use the bipartite graph-based network structure to describe users' behaviors and use PersonalRank to calculate the distance between campaigns and users to directly produce recommendation lists. Next, we integrate bipartite graph model and CF algorithm, and the correlation among the items set (the users set) is obtained by PersonalRank as the measurement of interest similarity. Experimental results show that recommender based on bipartite graph model achieves better performance on a sparse dataset. This paper proposes a method to solve the problem of sparse data, providing a new idea for generating recommen-

Directions for future works are as follows. (1) In terms of bipartite graph model,

PersonalRank is not the only algorithm, while other network algorithms are

those most popular users to the target campaign.

*Comprehensive comparison result of various algorithms.*

**7. Conclusion and prospects**

**Recommender List**

*Result of content-based recommender (*N *= 10).*

Bipartite graphbased CF

**Table 9.**

*Banking and Finance*

Bipartite graphbased CF

**Table 10.**

**210**

**length** *N*

**Number of neighbors** *K*

Cosine-based CF 5 40 0.10 0.35 2.33 1.052 Cosine-based CF 10 55 0.18 0.3 4.4 0.99 PersonalRank 5 Global 0.59 0.85 6.75 1.425 PersonalRank 10 Global 0.90 0.66 11.76 1.365

*K* **Recall (%) Precision (%) Coverage (%) Popularity** 0.10 0.17 1.17 1.698 0.11 0.18 1.11 1.72 0.12 0.19 1.06 1.739 **0.12 0.20 1.01 1.76**

> **Recall (%)**

5 30 0.41 0.59 5.19 1.641

10 30 0.64 0.47 9.54 1.532

**Precision (%)**

**Coverage (%)**

**Popularity**

dation list in crowdfunding platforms.

The authors declare no conflict of interest.
